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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "import os\n",
    "import numpy as np\n",
    "import sys\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.backend as K\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "# these are my working paths\n",
    "sys.path.append(r'/nesi/project/niwa00018/rampaln/High-res-interpretable-dl/src')\n",
    "# change to the directory of the \"src file\" in your directory\n",
    "os.chdir(r'/nesi/project/niwa00018/rampaln/High-res-interpretable-dl')\n",
    "# change directory to your repository of interest\n",
    "import tensorflow as tf\n",
    "from dask.diagnostics import ProgressBar\n",
    "import cmocean\n",
    "from models import train_model, complex_conv, simple_conv, predict, simple_dense, linear_complex_model\n",
    "from losses import gamma_loss_1d, gamma_mse_metric\n",
    "from prepare_data import format_features, prepare_training_dataset, create_test_train_split\n",
    "\n",
    "\n",
    "tf.random.set_seed(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.test.is_gpu_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here is where we select where the input and output data is the features we want to use\n",
    "config = dict(y = \"/nesi/project/niwa00018/rampaln/High-res-interpretable-dl/data/pacific_domain_mswep_daily_v2.nc\",\n",
    "              X = \"/nesi/project/niwa00018/rampaln/High-res-interpretable-dl/data/era5.nc\",\n",
    "             train_start = \"1982-01-01\",\n",
    "             train_end = \"2005-01-01\",\n",
    "             val_start = \"2005-02-01\",\n",
    "             val_end = \"2010-01-01\",\n",
    "             test_start = \"2010-01-01\",\n",
    "             test_end = \"2018-12-01\",\n",
    "             downscale_variables = ['w_850', 'u_850',\n",
    "             'v_850', 'q_850', 't_850','w_500', 'u_500',\n",
    "             'v_500', 'q_500', 't_500'])\n",
    "# you can modify any of the above features\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Training Data\n",
    "Here we load the training data from a configuration file and prepare it for training DL models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  1.1s\n",
      "[########################################] | 100% Completed |  1.2s\n",
      "[########################################] | 100% Completed |  1.8s\n",
      "[########################################] | 100% Completed |  0.4s\n",
      "[########################################] | 100% Completed |  0.2s\n",
      "[########################################] | 100% Completed |  0.2s\n"
     ]
    }
   ],
   "source": [
    "x_train, x_val, x_test, y_train, y_val, y_test = create_test_train_split(config)\n",
    "# load the training data\n",
    "x_train, x_test, x_val, y_train, y_test, y_val = prepare_training_dataset(x_train, x_val, x_test, y_train, y_val, y_test)      \n",
    "# modify the training data so that it is compatible with tensorflow and training\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model training hyperparameters\n",
    "Here are the model training hyperparameters used in this study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nesi/project/niwa00004/rampaln/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "initial_learning_rate =1e-4\n",
    "dropout = 0.6\n",
    "input_shape = x_train.shape[1:]\n",
    "output_shape = y_train.z.size\n",
    "hidden_layer_dense = 256\n",
    "batch_size =64\n",
    "kernel_size = 5\n",
    "layer_filters =[16,  64]\n",
    "epochs =500\n",
    "optimizer = tf.keras.optimizers.Adam(lr =initial_learning_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining Three Model Architectures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simple lienar model with mse loss\n",
    "#linear_model = simple_dense(dense_layers=[hidden_layer_dense, output_shape], dense_activation='selu', input_shape=input_shape, dropout=dropout)\n",
    "# linear model with Gamma loss\n",
    "# complex_linear = linear_complex_model(dense_layers=[hidden_layer_dense], dense_activation='selu', input_shape=input_shape, dropout=dropout,\n",
    "#                          output_shape=output_shape)\n",
    "# cnn model with gamma loss\n",
    "cnn_gamma = complex_conv(layer_filters=layer_filters, bn=True, padding='valid', kernel_size=(kernel_size,kernel_size),\n",
    "                pooling=True, dense_layers=[hidden_layer_dense], dense_activation='selu', input_shape=input_shape,\n",
    "                dropout=dropout, activation='selu', output_shape = output_shape)\n",
    "# cnn model with mse loss\n",
    "# simple_cnn = simple_conv(layer_filters=layer_filters, bn=True, padding='valid', kernel_size=(kernel_size,kernel_size),\n",
    "#                 pooling=True, dense_layers=[hidden_layer_dense, output_shape], dense_activation='selu', input_shape=input_shape,\n",
    "#                 dropout=dropout, activation='selu')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training with Gamma Loss Functions\n",
    "Here are the implementations with Gamma loss functions\n",
    "Note the Linear dense model (e.g. the models than are not CNNs) can be very unstable to train, so hyper parameter tuning is neccessary. \n",
    "\n",
    "Please train for more epochs (25 is only shown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 31, 51, 10)] 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 27, 47, 16)   4016        input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, 13, 23, 16)   0           conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 13, 23, 16)   64          average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 9, 19, 64)    25664       batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 4, 9, 64)     0           conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 4, 9, 64)     256         average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 2304)         0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 2304)         0           flatten[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 256)          590080      dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 15000)        3855000     dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 15000)        3855000     dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 15000)        3855000     dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, 1, 15000)     0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 1, 15000)     0           dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "reshape_2 (Reshape)             (None, 1, 15000)     0           dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 3, 15000)     0           reshape[0][0]                    \n",
      "                                                                 reshape_1[0][0]                  \n",
      "                                                                 reshape_2[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 12,185,080\n",
      "Trainable params: 12,184,920\n",
      "Non-trainable params: 160\n",
      "__________________________________________________________________________________________________\n",
      "WARNING:tensorflow:Keras is training/fitting/evaluating on array-like data. Keras may not be optimized for this format, so if your input data format is supported by TensorFlow I/O (https://github.com/tensorflow/io) we recommend using that to load a Dataset instead.\n",
      "Epoch 1/25\n",
      "132/132 [==============================] - 14s 22ms/step - loss: 1.8334 - gamma_mse_metric: 69.7939 - val_loss: 1.9517 - val_gamma_mse_metric: 60.6611\n",
      "Epoch 2/25\n",
      "132/132 [==============================] - 2s 17ms/step - loss: 1.8346 - gamma_mse_metric: 69.2575 - val_loss: 1.9540 - val_gamma_mse_metric: 61.5377\n",
      "Epoch 3/25\n",
      "132/132 [==============================] - 2s 17ms/step - loss: 1.8319 - gamma_mse_metric: 64.8270 - val_loss: 1.9526 - val_gamma_mse_metric: 60.0022\n",
      "Epoch 4/25\n",
      "132/132 [==============================] - 2s 15ms/step - loss: 1.8285 - gamma_mse_metric: 73.7052 - val_loss: 1.9527 - val_gamma_mse_metric: 59.9527\n",
      "Epoch 5/25\n",
      "132/132 [==============================] - 2s 16ms/step - loss: 1.8278 - gamma_mse_metric: 63.2306 - val_loss: 1.9532 - val_gamma_mse_metric: 62.2158\n",
      "Epoch 6/25\n",
      "132/132 [==============================] - 2s 16ms/step - loss: 1.8408 - gamma_mse_metric: 68.6958 - val_loss: 1.9549 - val_gamma_mse_metric: 62.2380\n"
     ]
    }
   ],
   "source": [
    "# # to train a linear dnese model with a gamma loss function use the following implementation \n",
    "# history, model = train_model(\n",
    "# complex_linear, [x_train, y_train], x_val = x_val.values, y_val = y_val.values,\n",
    "#                              loss = gamma_loss, epochs =25, batch_size=64,\n",
    "#                              optimizer = optimizer, model_weights_name = 'model_with_new_training_period.h5',\n",
    "#                             metrics =gamma_mse_metric)\n",
    "# # n\n",
    "history, cnn_gamma = train_model(\n",
    "cnn_gamma, [x_train.values, y_train.precipitation], x_val = x_val.values, y_val = y_val.precipitation.values,\n",
    "                             loss = gamma_loss_1d, epochs =25, batch_size=64,\n",
    "                             optimizer = optimizer, model_weights_name = 'model_with_new_training_period.h5',\n",
    "                            metrics =gamma_mse_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training with MSE Loss functions\n",
    "Here are the implementations with MSE loss funcitons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference for MSE LOss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference for GAMMA_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Keras is training/fitting/evaluating on array-like data. Keras may not be optimized for this format, so if your input data format is supported by TensorFlow I/O (https://github.com/tensorflow/io) we recommend using that to load a Dataset instead.\n",
      "102/102 [==============================] - 1s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "gamma_prediciton = predict(cnn_gamma, x_test, y_test.precipitation, batch_size=32, key ='precipitation', pred_name =\"test\", loss ='gamma' , thres =0.5)\n",
    "gamma_prediciton= gamma_prediciton.unstack()\n",
    "gamma_prediciton = gamma_prediciton.reindex(lon = sorted(gamma_prediciton.lon.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
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       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
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       "  grid-column: 1 / -1;\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lon: 100, time: 3257, lat: 150)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 165.1 165.2 165.3 165.4 ... 174.7 174.8 174.9 174.9\n",
       "  * time     (time) datetime64[ns] 2010-01-01 2010-01-02 ... 2018-12-01\n",
       "  * lat      (lat) float64 -10.05 -10.15 -10.25 -10.35 ... -24.75 -24.85 -24.95\n",
       "Data variables:\n",
       "    test     (time, lat, lon) float32 20.68 21.2 21.04 ... 16.46 16.61 16.83</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d21420b2-0ea0-48a4-9972-495457ff3378' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d21420b2-0ea0-48a4-9972-495457ff3378' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 100</li><li><span class='xr-has-index'>time</span>: 3257</li><li><span class='xr-has-index'>lat</span>: 150</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f1fd154b-8a99-49be-b508-5eb07ff04711' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f1fd154b-8a99-49be-b508-5eb07ff04711' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>165.1 165.2 165.3 ... 174.9 174.9</div><input id='attrs-d2a0bc1c-b990-4bb2-8171-7324a1c657ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d2a0bc1c-b990-4bb2-8171-7324a1c657ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff657332-d24f-4bcd-bed0-7419bb09878f' class='xr-var-data-in' type='checkbox'><label for='data-ff657332-d24f-4bcd-bed0-7419bb09878f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([165.050003, 165.150009, 165.250015, 165.350021, 165.449997, 165.550003,\n",
       "       165.650009, 165.750015, 165.850021, 165.949997, 166.050003, 166.150009,\n",
       "       166.250015, 166.350021, 166.449997, 166.550003, 166.650009, 166.750015,\n",
       "       166.850021, 166.949997, 167.050003, 167.150009, 167.250015, 167.350021,\n",
       "       167.449997, 167.550003, 167.650009, 167.750015, 167.850021, 167.949997,\n",
       "       168.050003, 168.150009, 168.250015, 168.350021, 168.449997, 168.550003,\n",
       "       168.650009, 168.750015, 168.850021, 168.949997, 169.050003, 169.150009,\n",
       "       169.250015, 169.350021, 169.449997, 169.550003, 169.650009, 169.750015,\n",
       "       169.850021, 169.949997, 170.050003, 170.150009, 170.250015, 170.350021,\n",
       "       170.449997, 170.550003, 170.650009, 170.750015, 170.850021, 170.949997,\n",
       "       171.050003, 171.150009, 171.250015, 171.350021, 171.449997, 171.550003,\n",
       "       171.650009, 171.750015, 171.850021, 171.949997, 172.050003, 172.150009,\n",
       "       172.250015, 172.350021, 172.449997, 172.550003, 172.650009, 172.750015,\n",
       "       172.850021, 172.949997, 173.050003, 173.150009, 173.250015, 173.350021,\n",
       "       173.449997, 173.550003, 173.650009, 173.750015, 173.850021, 173.949997,\n",
       "       174.050003, 174.150009, 174.250015, 174.350021, 174.449997, 174.550003,\n",
       "       174.650009, 174.750015, 174.850021, 174.949997])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-01-01 ... 2018-12-01</div><input id='attrs-26865a7b-3719-4bef-9340-8e085ffe54b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26865a7b-3719-4bef-9340-8e085ffe54b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88ebd6c4-55d6-4cc3-ad02-ba0b0c2a2a32' class='xr-var-data-in' type='checkbox'><label for='data-88ebd6c4-55d6-4cc3-ad02-ba0b0c2a2a32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2010-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-01-03T00:00:00.000000000&#x27;, ..., &#x27;2018-11-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-30T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-10.05 -10.15 ... -24.85 -24.95</div><input id='attrs-c7b58f7a-8d2e-468c-ac98-158259d27fb4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c7b58f7a-8d2e-468c-ac98-158259d27fb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c800ee9f-f4a1-4f8d-b516-e8f2466ca2a6' class='xr-var-data-in' type='checkbox'><label for='data-c800ee9f-f4a1-4f8d-b516-e8f2466ca2a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-10.05    , -10.149999, -10.250005, -10.350003, -10.450002, -10.55    ,\n",
       "       -10.649999, -10.750005, -10.850003, -10.950002, -11.05    , -11.149999,\n",
       "       -11.250005, -11.350003, -11.450002, -11.55    , -11.649999, -11.750005,\n",
       "       -11.850003, -11.950002, -12.05    , -12.149999, -12.250005, -12.350003,\n",
       "       -12.450002, -12.55    , -12.649999, -12.750005, -12.850003, -12.950002,\n",
       "       -13.05    , -13.149999, -13.250005, -13.350003, -13.450002, -13.55    ,\n",
       "       -13.649999, -13.750005, -13.850003, -13.950002, -14.05    , -14.149999,\n",
       "       -14.250005, -14.350003, -14.450002, -14.55    , -14.649999, -14.750005,\n",
       "       -14.850003, -14.950002, -15.05    , -15.149999, -15.250005, -15.350003,\n",
       "       -15.450002, -15.55    , -15.649999, -15.750005, -15.850003, -15.950002,\n",
       "       -16.049999, -16.149998, -16.250004, -16.350002, -16.450001, -16.549999,\n",
       "       -16.649998, -16.750004, -16.850002, -16.950001, -17.049999, -17.149998,\n",
       "       -17.250004, -17.350002, -17.450001, -17.549999, -17.649998, -17.750004,\n",
       "       -17.850002, -17.950001, -18.049999, -18.149998, -18.250004, -18.350002,\n",
       "       -18.450001, -18.549999, -18.649998, -18.750004, -18.850002, -18.950001,\n",
       "       -19.049999, -19.149998, -19.250004, -19.350002, -19.450001, -19.549999,\n",
       "       -19.649998, -19.750004, -19.850002, -19.950001, -20.049999, -20.149998,\n",
       "       -20.250004, -20.350002, -20.450001, -20.549999, -20.649998, -20.750004,\n",
       "       -20.850002, -20.950001, -21.049999, -21.149998, -21.250004, -21.350002,\n",
       "       -21.450001, -21.549999, -21.649998, -21.750004, -21.850002, -21.950001,\n",
       "       -22.049999, -22.149998, -22.250004, -22.350002, -22.450001, -22.549999,\n",
       "       -22.649998, -22.750004, -22.850002, -22.950001, -23.049999, -23.149998,\n",
       "       -23.250004, -23.350002, -23.450001, -23.549999, -23.649998, -23.750004,\n",
       "       -23.850002, -23.950001, -24.049999, -24.149998, -24.250004, -24.350002,\n",
       "       -24.450001, -24.549999, -24.649998, -24.750004, -24.850002, -24.950001])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2c58f251-39dd-458d-9a1a-c6eb97ce59b2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2c58f251-39dd-458d-9a1a-c6eb97ce59b2' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>test</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.68 21.2 21.04 ... 16.61 16.83</div><input id='attrs-2133f895-8cbf-45ea-acaa-2f4b993de24c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2133f895-8cbf-45ea-acaa-2f4b993de24c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9804c8ef-5922-42ea-9c73-df72309a6e15' class='xr-var-data-in' type='checkbox'><label for='data-9804c8ef-5922-42ea-9c73-df72309a6e15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm d-1</dd></dl></div><div class='xr-var-data'><pre>array([[[20.682154  , 21.200033  , 21.044874  , ..., 18.446634  ,\n",
       "         18.775341  , 18.217476  ],\n",
       "        [21.397507  , 21.311481  , 21.19084   , ..., 18.348667  ,\n",
       "         18.208508  , 18.357357  ],\n",
       "        [21.350828  , 21.390297  , 21.24248   , ..., 18.455318  ,\n",
       "         18.39222   , 18.512865  ],\n",
       "        ...,\n",
       "        [ 0.57598895,  0.5739708 ,  0.5954757 , ...,  0.4204479 ,\n",
       "          0.44259116,  0.4154282 ],\n",
       "        [ 0.5899562 ,  0.58148444,  0.5859913 , ...,  0.42392355,\n",
       "          0.43052217,  0.41669592],\n",
       "        [ 0.6407718 ,  0.64339596,  0.6288859 , ...,  0.42607176,\n",
       "          0.42905363,  0.42309326]],\n",
       "\n",
       "       [[13.472134  , 13.552119  , 13.785662  , ..., 13.651891  ,\n",
       "         13.876921  , 13.551763  ],\n",
       "        [13.8759775 , 13.6178255 , 13.902645  , ..., 13.476651  ,\n",
       "         13.601146  , 13.637717  ],\n",
       "        [13.970583  , 13.884986  , 13.897385  , ..., 13.520336  ,\n",
       "         13.55611   , 13.584092  ],\n",
       "...\n",
       "        [ 6.1527095 ,  6.8589883 ,  7.1243186 , ..., 13.780232  ,\n",
       "         13.779805  , 11.609132  ],\n",
       "        [ 6.436137  ,  7.151292  ,  6.834438  , ..., 14.190106  ,\n",
       "         13.989361  , 11.7364025 ],\n",
       "        [ 6.8339295 ,  6.9631014 ,  6.7942705 , ..., 15.755108  ,\n",
       "         15.108254  , 15.072124  ]],\n",
       "\n",
       "       [[ 4.362233  ,  4.292886  ,  4.27377   , ...,  6.438411  ,\n",
       "          6.3497653 ,  6.488985  ],\n",
       "        [ 4.300832  ,  4.2983665 ,  4.2754784 , ...,  6.707321  ,\n",
       "          6.6857834 ,  6.680428  ],\n",
       "        [ 4.3111405 ,  4.252762  ,  4.4003143 , ...,  6.808494  ,\n",
       "          6.8176126 ,  6.942019  ],\n",
       "        ...,\n",
       "        [ 0.24328536,  0.259883  ,  0.26829162, ..., 16.773014  ,\n",
       "         17.224539  , 16.53429   ],\n",
       "        [ 0.24238206,  0.26400596,  0.26321474, ..., 17.434914  ,\n",
       "         17.276016  , 16.412855  ],\n",
       "        [ 0.27942872,  0.283147  ,  0.28032187, ..., 16.458809  ,\n",
       "         16.613678  , 16.833006  ]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fae4c05c-246a-4171-b258-e6ca3f2869fa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fae4c05c-246a-4171-b258-e6ca3f2869fa' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lon: 100, time: 3257, lat: 150)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 165.1 165.2 165.3 165.4 ... 174.7 174.8 174.9 174.9\n",
       "  * time     (time) datetime64[ns] 2010-01-01 2010-01-02 ... 2018-12-01\n",
       "  * lat      (lat) float64 -10.05 -10.15 -10.25 -10.35 ... -24.75 -24.85 -24.95\n",
       "Data variables:\n",
       "    test     (time, lat, lon) float32 20.68 21.2 21.04 ... 16.46 16.61 16.83"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gamma_prediciton "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating one Dataset for all the models to evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_predictions['experiments'] = (('experiments'),['Simple_cnn','simple_dense', 'cnn_gamma', 'linear_gamma'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:        (lon: 100, time: 3257, lat: 150)\n",
       "Coordinates:\n",
       "  * lon            (lon) float64 165.1 165.2 165.3 165.4 ... 174.8 174.9 174.9\n",
       "  * time           (time) datetime64[ns] 2010-01-01 2010-01-02 ... 2018-12-01\n",
       "  * lat            (lat) float64 -10.05 -10.15 -10.25 ... -24.75 -24.85 -24.95\n",
       "Data variables:\n",
       "    precipitation  (time, lat, lon) float32 17.5 14.25 12.69 ... 23.94 28.12\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 1.9.5 (http://mpimet.mpg.de/...\n",
       "    history:      Thu Nov 16 23:00:29 2023: cdo sellonlatbox,120,240,-50,30 t...\n",
       "    Conventions:  CF-1.6\n",
       "    CDO:          Climate Data Operators version 1.9.5 (http://mpimet.mpg.de/...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bd8114cf-04dd-48e3-822a-39896d0a68ac' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd8114cf-04dd-48e3-822a-39896d0a68ac' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 100</li><li><span class='xr-has-index'>time</span>: 3257</li><li><span class='xr-has-index'>lat</span>: 150</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-90152c0b-5109-40dd-b1c2-bdd21979be68' class='xr-section-summary-in' type='checkbox'  checked><label for='section-90152c0b-5109-40dd-b1c2-bdd21979be68' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>165.1 165.2 165.3 ... 174.9 174.9</div><input id='attrs-9e98ddee-c55e-4df3-b2d0-d09304e98af1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e98ddee-c55e-4df3-b2d0-d09304e98af1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b44e9737-0ed2-43d4-be3d-68d689a2c901' class='xr-var-data-in' type='checkbox'><label for='data-b44e9737-0ed2-43d4-be3d-68d689a2c901' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([165.050003, 165.150009, 165.250015, 165.350021, 165.449997, 165.550003,\n",
       "       165.650009, 165.750015, 165.850021, 165.949997, 166.050003, 166.150009,\n",
       "       166.250015, 166.350021, 166.449997, 166.550003, 166.650009, 166.750015,\n",
       "       166.850021, 166.949997, 167.050003, 167.150009, 167.250015, 167.350021,\n",
       "       167.449997, 167.550003, 167.650009, 167.750015, 167.850021, 167.949997,\n",
       "       168.050003, 168.150009, 168.250015, 168.350021, 168.449997, 168.550003,\n",
       "       168.650009, 168.750015, 168.850021, 168.949997, 169.050003, 169.150009,\n",
       "       169.250015, 169.350021, 169.449997, 169.550003, 169.650009, 169.750015,\n",
       "       169.850021, 169.949997, 170.050003, 170.150009, 170.250015, 170.350021,\n",
       "       170.449997, 170.550003, 170.650009, 170.750015, 170.850021, 170.949997,\n",
       "       171.050003, 171.150009, 171.250015, 171.350021, 171.449997, 171.550003,\n",
       "       171.650009, 171.750015, 171.850021, 171.949997, 172.050003, 172.150009,\n",
       "       172.250015, 172.350021, 172.449997, 172.550003, 172.650009, 172.750015,\n",
       "       172.850021, 172.949997, 173.050003, 173.150009, 173.250015, 173.350021,\n",
       "       173.449997, 173.550003, 173.650009, 173.750015, 173.850021, 173.949997,\n",
       "       174.050003, 174.150009, 174.250015, 174.350021, 174.449997, 174.550003,\n",
       "       174.650009, 174.750015, 174.850021, 174.949997])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-01-01 ... 2018-12-01</div><input id='attrs-28a68f13-1c1b-4ed8-914f-dfc0a2b2744b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28a68f13-1c1b-4ed8-914f-dfc0a2b2744b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35c33849-24db-47c6-9b08-ef2848533d44' class='xr-var-data-in' type='checkbox'><label for='data-35c33849-24db-47c6-9b08-ef2848533d44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2010-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-01-03T00:00:00.000000000&#x27;, ..., &#x27;2018-11-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-30T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-10.05 -10.15 ... -24.85 -24.95</div><input id='attrs-67aff5e7-42b8-4aa7-b121-02303c4cff6a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67aff5e7-42b8-4aa7-b121-02303c4cff6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16b536bb-d2e3-404b-9b17-bd00539a8ff6' class='xr-var-data-in' type='checkbox'><label for='data-16b536bb-d2e3-404b-9b17-bd00539a8ff6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-10.05    , -10.149999, -10.250005, -10.350003, -10.450002, -10.55    ,\n",
       "       -10.649999, -10.750005, -10.850003, -10.950002, -11.05    , -11.149999,\n",
       "       -11.250005, -11.350003, -11.450002, -11.55    , -11.649999, -11.750005,\n",
       "       -11.850003, -11.950002, -12.05    , -12.149999, -12.250005, -12.350003,\n",
       "       -12.450002, -12.55    , -12.649999, -12.750005, -12.850003, -12.950002,\n",
       "       -13.05    , -13.149999, -13.250005, -13.350003, -13.450002, -13.55    ,\n",
       "       -13.649999, -13.750005, -13.850003, -13.950002, -14.05    , -14.149999,\n",
       "       -14.250005, -14.350003, -14.450002, -14.55    , -14.649999, -14.750005,\n",
       "       -14.850003, -14.950002, -15.05    , -15.149999, -15.250005, -15.350003,\n",
       "       -15.450002, -15.55    , -15.649999, -15.750005, -15.850003, -15.950002,\n",
       "       -16.049999, -16.149998, -16.250004, -16.350002, -16.450001, -16.549999,\n",
       "       -16.649998, -16.750004, -16.850002, -16.950001, -17.049999, -17.149998,\n",
       "       -17.250004, -17.350002, -17.450001, -17.549999, -17.649998, -17.750004,\n",
       "       -17.850002, -17.950001, -18.049999, -18.149998, -18.250004, -18.350002,\n",
       "       -18.450001, -18.549999, -18.649998, -18.750004, -18.850002, -18.950001,\n",
       "       -19.049999, -19.149998, -19.250004, -19.350002, -19.450001, -19.549999,\n",
       "       -19.649998, -19.750004, -19.850002, -19.950001, -20.049999, -20.149998,\n",
       "       -20.250004, -20.350002, -20.450001, -20.549999, -20.649998, -20.750004,\n",
       "       -20.850002, -20.950001, -21.049999, -21.149998, -21.250004, -21.350002,\n",
       "       -21.450001, -21.549999, -21.649998, -21.750004, -21.850002, -21.950001,\n",
       "       -22.049999, -22.149998, -22.250004, -22.350002, -22.450001, -22.549999,\n",
       "       -22.649998, -22.750004, -22.850002, -22.950001, -23.049999, -23.149998,\n",
       "       -23.250004, -23.350002, -23.450001, -23.549999, -23.649998, -23.750004,\n",
       "       -23.850002, -23.950001, -24.049999, -24.149998, -24.250004, -24.350002,\n",
       "       -24.450001, -24.549999, -24.649998, -24.750004, -24.850002, -24.950001])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7dfb7bc1-8784-4cd2-b1a6-4742b12b7db2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7dfb7bc1-8784-4cd2-b1a6-4742b12b7db2' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>precipitation</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>17.5 14.25 12.69 ... 23.94 28.12</div><input id='attrs-ee063beb-6d27-411d-a21c-0e69cf8606be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee063beb-6d27-411d-a21c-0e69cf8606be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dcdda38-13be-4509-bd35-e9b09c21bc81' class='xr-var-data-in' type='checkbox'><label for='data-7dcdda38-13be-4509-bd35-e9b09c21bc81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm d-1</dd></dl></div><div class='xr-var-data'><pre>array([[[1.750000e+01, 1.425000e+01, 1.268750e+01, ..., 2.287500e+01,\n",
       "         2.025000e+01, 1.756250e+01],\n",
       "        [1.818750e+01, 1.543750e+01, 1.425000e+01, ..., 2.468750e+01,\n",
       "         2.143750e+01, 1.806250e+01],\n",
       "        [1.793750e+01, 1.443750e+01, 1.337500e+01, ..., 2.568750e+01,\n",
       "         2.350000e+01, 2.037500e+01],\n",
       "        ...,\n",
       "        [1.250000e-01, 1.250000e-01, 1.250000e-01, ..., 6.875000e-01,\n",
       "         6.875000e-01, 6.250000e-01],\n",
       "        [1.250000e-01, 1.250000e-01, 1.250000e-01, ..., 6.875000e-01,\n",
       "         6.875000e-01, 6.250000e-01],\n",
       "        [6.250000e-02, 0.000000e+00, 0.000000e+00, ..., 5.625000e-01,\n",
       "         5.625000e-01, 5.625000e-01]],\n",
       "\n",
       "       [[2.250000e+00, 2.437500e+00, 3.875000e+00, ..., 2.062500e+00,\n",
       "         1.625000e+00, 1.437500e+00],\n",
       "        [3.812500e+00, 3.562500e+00, 4.437500e+00, ..., 2.250000e+00,\n",
       "         1.875000e+00, 1.687500e+00],\n",
       "        [4.250000e+00, 4.312500e+00, 5.250000e+00, ..., 2.062500e+00,\n",
       "         1.937500e+00, 1.750000e+00],\n",
       "...\n",
       "        [0.000000e+00, 0.000000e+00, 0.000000e+00, ..., 2.312500e+01,\n",
       "         2.262500e+01, 1.762500e+01],\n",
       "        [0.000000e+00, 0.000000e+00, 0.000000e+00, ..., 2.243750e+01,\n",
       "         2.175000e+01, 1.656250e+01],\n",
       "        [0.000000e+00, 0.000000e+00, 0.000000e+00, ..., 2.287500e+01,\n",
       "         2.212500e+01, 1.625000e+01]],\n",
       "\n",
       "       [[1.000000e+00, 1.375000e+00, 1.375000e+00, ..., 2.343750e+01,\n",
       "         2.356250e+01, 2.518750e+01],\n",
       "        [1.125000e+00, 1.562500e+00, 1.562500e+00, ..., 2.312500e+01,\n",
       "         2.256250e+01, 2.343750e+01],\n",
       "        [1.125000e+00, 1.562500e+00, 1.562500e+00, ..., 2.256250e+01,\n",
       "         2.150000e+01, 2.206250e+01],\n",
       "        ...,\n",
       "        [0.000000e+00, 6.250000e-02, 6.250000e-02, ..., 2.781250e+01,\n",
       "         2.837500e+01, 3.162500e+01],\n",
       "        [0.000000e+00, 6.250000e-02, 6.250000e-02, ..., 2.468750e+01,\n",
       "         2.575000e+01, 3.037500e+01],\n",
       "        [0.000000e+00, 6.250000e-02, 6.250000e-02, ..., 2.318750e+01,\n",
       "         2.393750e+01, 2.812500e+01]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e4a6f9b-ea11-4249-8708-12032e3e9a0e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e4a6f9b-ea11-4249-8708-12032e3e9a0e' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.5 (http://mpimet.mpg.de/cdi)</dd><dt><span>history :</span></dt><dd>Thu Nov 16 23:00:29 2023: cdo sellonlatbox,120,240,-50,30 tmp_MSWEP_daily_pr_1982-2018_global.nc MSWEP_SP_daily_pr_1982-2018.nc</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.5 (http://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:        (lon: 100, time: 3257, lat: 150)\n",
       "Coordinates:\n",
       "  * lon            (lon) float64 165.1 165.2 165.3 165.4 ... 174.8 174.9 174.9\n",
       "  * time           (time) datetime64[ns] 2010-01-01 2010-01-02 ... 2018-12-01\n",
       "  * lat            (lat) float64 -10.05 -10.15 -10.25 ... -24.75 -24.85 -24.95\n",
       "Data variables:\n",
       "    precipitation  (time, lat, lon) float32 17.5 14.25 12.69 ... 23.94 28.12\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 1.9.5 (http://mpimet.mpg.de/...\n",
       "    history:      Thu Nov 16 23:00:29 2023: cdo sellonlatbox,120,240,-50,30 t...\n",
       "    Conventions:  CF-1.6\n",
       "    CDO:          Climate Data Operators version 1.9.5 (http://mpimet.mpg.de/..."
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2aaf16915340>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gt.precipitation.groupby('time.year').max().mean(\"year\").plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2aaf16424940>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gamma_prediciton.test.groupby('time.year').max().mean(\"year\").plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "gt = y_test.unstack()\n",
    "gt = gt.reindex(lon = sorted(gt.lon.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation Coefficient in Time Evalation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrs2 = xr.corr(gt.precipitation, gamma_prediciton.test, dim =\"time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "date = \"2016-03-27\"\n",
    "fig, ax = plt.subplots(1,2, figsize = (8, 5), subplot_kw = dict(projection = ccrs.PlateCarree(central_longitude = 171.77)))\n",
    "ax = ax.ravel()\n",
    "cbar_dict = dict(shrink =0.8, label = 'Daily Rainfall (mm/day)')\n",
    "exps = ['Simple_cnn','simple_dense', 'cnn_gamma', 'linear_gamma']\n",
    "\n",
    "gamma_prediciton.sel(time =date)['test'].plot(vmin =0, vmax =100,   cmap ='cmo.rain', ax = ax[0],\n",
    "                                                                        add_colorbar =False,transform = ccrs.PlateCarree())                                            \n",
    "\n",
    "cs = gt.sel(time =date).precipitation.plot(vmin =0, vmax =100, cmap ='cmo.rain', ax = ax[-1],\n",
    "                                                                            add_colorbar =False,transform = ccrs.PlateCarree())          \n",
    "[axes.set_title('') for axes in ax]\n",
    "[axes.coastlines('10m') for axes in ax]\n",
    "ax4 = fig.add_axes([0.1, 0.05, 0.8, 0.03])\n",
    "cbar = fig.colorbar(cs, cax=ax4, orientation ='horizontal')\n",
    "cbar.set_label('Rainfall (mm/day)', fontsize =15)\n",
    "#ax[0].set_title('Linear CNN')\n",
    "#ax[1].set_title('Linear Dense')\n",
    "ax[0].set_title('Gamma CNN')\n",
    "#ax[3].set_title('Gamma Dense')\n",
    "ax[-1].set_title('Ground Truth')\n",
    "# #[axes.add_feature(feature, zorder =5, color ='white') for axes in ax]\n",
    "# ax[-1].set_title('Observations')\n",
    "# ax[1].set_title('Non-linear Gamma Dense')\n",
    "# ax[2].set_title('Linear Dense')\n",
    "# ax[0].set_title('Non-linear Dense')\n",
    "# #ax[1].set_title('Non-linear Gamma Dense')\n",
    "# ax[3].set_title('CNN')\n",
    "# ax[-2].set_title('CNN Gamma')\n",
    "def remove_spines(ax):\n",
    "    ax.set_frame_on(False)\n",
    "    #fig.tight_layout()\n",
    "[remove_spines(axes) for axes in ax]\n",
    "# #fig.savefig('Comparison_of_simple_downsclaing_methods.pdf', dpi =300)\n",
    "#fig.savefig('Comparison_of_simple_downsclaing_methods.png', dpi =300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "My_env",
   "language": "python",
   "name": "nellys_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
